Abstract
We present a novel iterative, edit-based approach to unsupervised sentence simplification. Our model is guided by a scoring function involving fluency, simplicity, and meaning preservation. Then, we iteratively perform word and phrase-level edits on the complex sentence. Compared with previous approaches, our model does not require a parallel training set, but is more controllable and interpretable. Experiments on Newsela and WikiLarge datasets show that our approach is nearly as effective as state-of-the-art supervised approaches.- Anthology ID:
- 2020.acl-main.707
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7918–7928
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.707
- DOI:
- 10.18653/v1/2020.acl-main.707
- Cite (ACL):
- Dhruv Kumar, Lili Mou, Lukasz Golab, and Olga Vechtomova. 2020. Iterative Edit-Based Unsupervised Sentence Simplification. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7918–7928, Online. Association for Computational Linguistics.
- Cite (Informal):
- Iterative Edit-Based Unsupervised Sentence Simplification (Kumar et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2020.acl-main.707.pdf
- Code
- ddhruvkr/Edit-Unsup-TS
- Data
- Newsela, TurkCorpus, WikiLarge